The concept of the digital twin has been around for several years, but it is just now coming to the forefront thanks to the Internet of Things (IoT). The digital twin has a wide range of uses, from validating models with real-world data, planning its production processes, and predicting failure out in the field.
The digital twin allows design engineers to understand the product, predict its process through design, manufacturing, and use, and optimize all of the processes along the way. (Source Siemens PLM)
Shankar Raman, director of portfolio development for digital manufacturing at Siemens PLM, notes there are three things about the digital twin that makes the case for its value. “I have been to multiple shows and exhibit floors and people are still asking what the digital twin is and how it enables digitization,” Raman told Design News. “The key is that there are the three factors that matter in the digital twin. The digital twin allows us to understand, to predict, and to optimize. Those three components of the digital twin drive positive business outcomes.”
Using the Digital Twin to Understand Product Design and Production
The data captured and managed in the digital twin offers a wealth of information about the product, its simulations, its composition, its production processes, and its expected behavior out in the field. All of this data constitutes the “understanding” of the product. “Understanding is a matter of looking at the customers’ needs from predictability and cost. You look at the product from design and manufacturing to what features it might have, and its producibility,” said Raman. “Do we have the right facility and supply chain? What capital investment do we need. The answers to all of those questions become the understanding.”
Using the Digital Twin for Prediction
The second part of the digital twin’s value, according to Raman, is its predictive capabilities. “We talk about predicting product behavior based on conditions and constraints. I want to be able to accurately predict how the product will behave in different conditions and environments. I also want to predict plant operations during its production. If I’m in a brownfield plant versus a greenfield plant, I want to be able to predict what I’ll need in equipment and whether there will be critical acquisitions required for production.”
The digital twin collects and manages the data required to predict how the product will behave from production through its life in the field. “IoT plays a role in prediction over the life of the product. Can I gather enough information through IoT so I can predict when it’s going to fail?” said Raman. “Can I predict how my equipment will perform? Can I predict the OEE on the plant floor? Can I monitor the fleet of products in the field and predict when I’ll need to add a server to support the appropriate MRO? Can I predict the product’s failure and make sure I have MRO in the field?”
Optimizing with the Digital Twin
The third area of value for the digital twin is its ability to support optimization. “You can use the digital twin to optimize the product, the plant, and the product’s performance in the field. We’ve all heard of generative design and what it offers in weight reduction, all calculated around strength. I want to use this to optimize my product’s capability and take into consideration all of the pieces of the product,” said Raman. “In operations I want to make trade-offs and automate the production processes. I want to replace a human with a robot or add a robot. I want to make the decision by simulating it so I can optimize my capital investment. The digital twin makes all of that possible.”
Rob Spiegel has covered automation and control for 17 years, 15 of them for Design News. Other topics he has covered include supply chain technology, alternative energy, and cyber security. For 10 years, he was owner and publisher of the food magazine Chile Pepper.
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